Differential privacy

Definition

Differential privacy (DP) is a mathematically rigorous framework for releasing statistical information about datasets while protecting the privacy of individual data subjects. It enables a data holder to share aggregate patterns of the group while limiting information that is leaked about specific individuals. This is done by injecting carefully calibrated noise into statistical computations such that the utility of the statistic is preserved while provably limiting what can be inferred about any individual in the dataset.

Related concepts

Adam D. Smith Additive noise mechanisms Apple Inc.Boolean algebra Cell suppression Coin flipping Cryptanalysis Cynthia Dwork Data analysis Data sharing Denormal numbers Differential privacy composition theorems Differentially private analysis of graphs Dorothy Denning Double-precision floating-point format Exponential mechanism (differential privacy)Falsifiability Floating-point arithmetic Frank McSherry Fuzzing Gaussian distribution GitHub Google Gödel Prize Hamming distance IOS 10 Image (mathematics)Implementations of differentially private analyses Intelligent personal assistant Irit Dinur Journal of the American Statistical Association K-anonymity Kobbi Nissim L2 norm Laplace distribution Leaky abstraction Local differential privacy Mayer D. Schwartz Meta.com Metric space Micah Altman Microdata (statistics)NP-hardness Open access Peter J. Denning Privacy Probability distribution Protected health information Quasi-identifier Randomized algorithm Randomized response Randomness Real number Reconstruction attack Semantic Scholar Social science Software testing Standard deviation Statistical Statistical database Taxicab geometry Taylor & Francis Timing attack Tore Dalenius United States Census Bureau

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